Code-assistants that generate, refactor, and explain code contextually
JetBrains AI is an integrated code-assistant from JetBrains that provides in-IDE code generation, refactoring suggestions, and contextual explanations using models like CodeGen and licensed LLMs. It is designed for professional developers working in JetBrains IDEs (e.g., IntelliJ IDEA, PyCharm) who need code completion, tests, and documentation help. Pricing includes a free tier with limited in-IDE usage and paid Team/Enterprise plans for shared quotas and advanced controls.
JetBrains AI is a code-assistant that lives inside JetBrains IDEs to help developers generate, refactor, and explain code. The primary capability is context-aware code completions, whole-function generation, and transformation suggestions within IntelliJ-based IDEs. Its key differentiator is native integration with JetBrains IDE tooling and project context (indexing, inspections, and local files), serving individual developers and engineering teams. JetBrains AI supports privacy controls and on-prem options for enterprises. Pricing is accessible via a free tier for limited use and paid subscriptions for professional teams and enterprises.
JetBrains AI is JetBrains’ native code-assistant that integrates directly into IntelliJ-based IDEs (IntelliJ IDEA, PyCharm, WebStorm, CLion and others). Launched as part of JetBrains’ broader toolset expansion, it positions itself as an in-IDE augmentation layer that uses both cloud-hosted and configurable model options while leveraging the IDE’s project index, inspections, and type information. The core value proposition is reducing boilerplate work and accelerating development by producing context-aware suggestions that understand project symbols, dependency graphs, and existing tests, rather than working only from an isolated prompt.
Key features include context-aware code completion and whole-function generation that use the current file and project context to produce code snippets and implementations. The tool also provides explain-and-annotate capabilities: you can ask for line-by-line explanations, suggested refactorings, or documentation generation tied to detected types and usages. JetBrains AI offers test generation and mutation support, where it can create unit tests from function signatures and inferred behavior. The assistant integrates with IDE inspections and quick-fixes, meaning some suggestions are surfaced as regular IDE intents (Alt+Enter style) rather than through a separate chat window. There are also settings for model selection and privacy: teams can choose hosted JetBrains models or configure external LLM endpoints and control what project data is sent.
Pricing is offered in a freemium model plus paid subscriptions. JetBrains provides a free tier with limited monthly usage for individual users to try in-IDE features; exact monthly limits are indicated on JetBrains’ pricing page and may be restricted to feature trials and capped quota. Paid tiers include a Professional/Team subscription priced per user per month with full access to in-IDE completions, team quota sharing, and administrative controls; Enterprise options add on-premises and SSO/SCIM support with custom contracts. JetBrains often bundles AI options into its IDE license or as an add-on; customers should consult jetbrains.com/ai for current per-user prices and enterprise licensing details, as exact amounts and billing cycles are published there and updated periodically.
Developers, QA engineers, and engineering managers use JetBrains AI in real-world workflows such as generating boilerplate code, accelerating test coverage, and explaining unfamiliar code paths. For example, a backend engineer (Senior Java Developer) can generate repository-layer code and unit tests from method signatures to cut hours off scaffolding, while a QA automation engineer can produce data-driven test templates from existing function behavior. Compared to standalone web-based copilots, JetBrains AI's main distinction is its deep IDE integration and use of the project index, which makes it more context-aware than generic chat-model integrations like GitHub Copilot for some workflows.
Three capabilities that set JetBrains AI apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Limited monthly quota for in-IDE suggestions and trial features | Individual users evaluating features |
| Team / Professional | Exact per-user price listed on site | Full in-IDE access, shared team quota, admin controls | Small teams needing shared AI quota and controls |
| Enterprise | Custom | Includes SSO, SCIM, on-prem or private endpoint options | Large orgs needing compliance and private hosting |
Copy these into JetBrains AI as-is. Each targets a different high-value workflow.
Role: You are JetBrains AI, the in-IDE code assistant aware of the current file and project. Constraints: Inspect the Java method currently open or at caret; assume dependencies can be mocked with Mockito; produce tests using JUnit 5; cover typical, edge, and error cases; keep tests compile-ready and idiomatic. Output format: Provide a single Java test class file content with package, imports, @BeforeEach setup, and 3–6 @Test or @ParameterizedTest methods. Example: show one test method named testMethod_whenCondition_thenExpected(). Do not modify production code.
Role: You are JetBrains AI, integrated with the project context. Constraints: Given a Python dataclass or Pydantic model in the active editor, produce: (1) a to_dict() serializer that handles nested models and datetime objects (ISO8601), (2) a from_dict() classmethod that validates required fields and types, and (3) a concise docstring for both methods. Output format: return a single code block containing the updated model class with both methods and example usage at the bottom. Example: show from_dict({'id':1, 'created_at':'2023-01-01T00:00:00Z'}) -> instance.
Role: You are JetBrains AI acting as a backend engineer familiar with the project. Constraints: Target database: PostgreSQL; source schema version and target version are specified in the editor or inputs; produce a safe, reversible SQL migration (UP and DOWN) and list of application code changes required (file paths, method names) to accommodate schema changes; include data-migration steps and performance considerations. Output format: 1) SQL file content (BEGIN/COMMIT, idempotent where possible), 2) a checklist of code edits with exact line snippets to change, 3) a short rollback plan. Example: ALTER TABLE ...
Role: You are JetBrains AI with full project index access. Constraints: Scan the project for uses of a specified deprecated API symbol (provide symbol name or caret location), propose a modern replacement API, and generate automated code-transform patches limited to src/ directories; preserve existing behavior and tests. Output format: 1) summary table of files and lines to change, 2) a set of unified diff patches (git apply format) for each file, 3) one example before/after snippet. Example: replace OldClient.connect() -> NewClient.openConnection(config).
Role: You are JetBrains AI acting as a Senior Backend Architect with security expertise. Multi-step: (1) propose a REST resource design for the given domain (from editor or brief), including endpoints, HTTP methods, auth scopes, and error models; (2) generate language-specific DTOs (Java or Kotlin) with validation annotations and sanitized fields; (3) produce OpenAPI fragment for these endpoints; (4) provide migration notes for existing clients. Constraints: follow OWASP REST security practices, prefer immutable DTOs, and include field-level input validation. Output format: numbered sections for API design, DTO code blocks, OpenAPI YAML fragment, and migration notes. Example: include a sample POST request/response.
Role: You are JetBrains AI as a security engineer integrated with the IDE. Multi-step task: analyze the current file or selected project scope for SQL injection, command injection, XSS, and insecure deserialization patterns; for each finding provide: (a) description and exact code location, (b) severity and CWE mapping, (c) a minimal, secure code fix (patch/gist) with unit test demonstrating the fix, and (d) CI gate suggestion (inspection rule or static analyzer config). Constraints: do not produce false positives—only report defensible issues. Output format: a numbered list of findings with subitems (a–d) and code patches in diff format. Provide one short example finding as a pattern match.
Choose JetBrains AI over GitHub Copilot if you prioritize deep IDE project-index context and enterprise private-endpoint controls.
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